Improving the accuracy of cardiac DTI by averaging the complex data

Andrew D Scott,Dudley J Pennell,Sonia Nielles-Vallespin,David Firmin,Pedro Ferreira,Laura-Ann Mcgill

doi:10.1186/1532-429x-17-s1-o38

Abstract

Background When performing cardiac diffusion tensor imaging (cDTI) multiple averages are typically acquired to compensate for the low signal to noise ratio of the individual images. However, the potential for reducing noise in low signal areas is not fully realized when the averaging is performed on the magnitude data[1]. Averaging the complex cDTI data is not straightforward as the diffusion weighting introduces a different, spatially varying phase across each image. In this work we use simulations to demonstrate the benefits available when using complex averaging and then develop an algorithm for performing complex averaging of in-vivo cDTI data, which accounts for the induced phase variations.

Highlights

When performing cardiac diffusion tensor imaging multiple averages are typically acquired to compensate for the low signal to noise ratio of the individual images
To compare the performance of magnitude and complex averaging in the presence of noise, simulated noisy diffusion weighted images of the left ventricle were calculated with b-values from 50-3000smm-2 based on previously published in-vivo cardiac diffusion tensor imaging (cDTI) data[2]
Magnitude or complex averaging was performed before processing the images with the software we typically use for in-vivo cDTI data

Summary

Background

When performing cardiac diffusion tensor imaging (cDTI) multiple averages are typically acquired to compensate for the low signal to noise ratio of the individual images. The potential for reducing noise in low signal areas is not fully realized when the averaging is performed on the magnitude data[1]. Averaging the complex cDTI data is not straightforward as the diffusion weighting introduces a different, spatially varying phase across each image. In this work we use simulations to demonstrate the benefits available when using complex averaging and develop an algorithm for performing complex averaging of in-vivo cDTI data, which accounts for the induced phase variations

Methods

Results

Conclusions